Relativistic Quantum Information: Applications and foundations in Quantum Information, Relativity and Machine Learning

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Over the past decade, a new field of high research intensity has emerged: Relativistic Quantum Information (RQI). RQI brings together the two pillars of modern physics, general relativity and quantum theory, with information theory. The results in the field of RQI range from new insights into the laws of Nature (e.g. black hole physics and cosmology) all the way to concrete applications in quantum computing and quantum-secured communication. In particular, the study of relativistic effects in quantum information processing has recently become more important due to improvements both in theory and in technology. Experiments in quantum optics, superconducting circuits and Bose-Einstein condensates can now probe relativistic influences on quantum information. Protocols that distribute entanglement over hundreds of kilometers are now reaching regimes where relativistic effects are becoming important, and satellite experiments are being developed that will measure gravitational effects on quantum entanglement. This proposal seeks to investigate some of the key theoretical and applied questions in the field of RQI: Which relativistic settings have an advantage over non-relativistic ones in quantum information technologies such as quantum cryptography or quantum computing? Can the fluctuations of a quantum field provide a practical and renewable source of quantum entanglement?  Concretely, the proposed research investigates the processing of quantum information in still unexplored regimes in quantum field theory, ultra-fast quantum optics and superconducting circuits where both Einstein's relativity and quantum theory are necessary to understand the processing and flow of information. Furthermore, the proposed research explores the use of deep learning and other machine learning techniques to study measurements and correlations in quantum field theory. In particular this proposal will investigate how neural networks can be applied to the problem of classifying non-local features of QFTs using probe data from measurements obtained coupling particle detectors locally (in space and time) to the field. The long term objective is to develop a new way of exploring measurement theory in relativistic quantum mechanics and field theory connecting deep notions in theoretical physic such as holography in quantum gravity and the entanglement in quantum field theories with the latest techniques in data processing. These studies will advance our fundamental knowledge of quantum theory and relativity. At the same time, will potentially have a significant long-term impact on Canada's economy and industry developing the Canadian expertise in machine learning and in quantum technologies. Concretely, applying knowledge acquired through the theoretical studies to the development of applications in quantum computing, quantum-secured communication and the development of measurement devices with sensitivities much beyond the limits of current technologies.